1. Technical Field
The invention relates generally to computerized discrete event simulation techniques. More particularly, the invention relates to a method of providing capacity planning information for IT systems by modeling them as discrete event simulations on a continual basis.
2. Description of the Prior Art
Predicting capacity requirements for Information Technology (IT) systems using simulation models is critical to the efficient and flexible deployment of computer and network assets to meet rapidly changing business environments. Complex system models that are devised using discrete event simulation software, such as simulation models, provide a projection of future requirements by representing the relationships between collections of parameters that describe traditional computer or communications systems measures, such as CPU speeds, queue lengths, and network link bandwidths, as well as many others, with more abstract, business-centered values, such as projected increases in business transaction volumes, changes in workload distribution, or increased worker efficiencies through enhanced skills training.
The current processes and functions to define, develop and utilize simulation modeling for capacity planning are time-consuming, very complex and, typically, pursued uniquely for each business situation or problem. Developing, executing, and interpreting the results produced by simulation models requires extensive involvement of highly skilled technical resources. In order to satisfy an increasing demand for capacity planning simulation models, a more cost effective and streamlined approach is necessary. Specifically the following challenges must be addressed:
Reduce the time to create an effective model that addresses specific system performance or capacity questions. Typically, it takes from eight to twelve weeks to develop a single discrete event simulation model that is designed to answer a concise set of questions concerning the future performance of a specific system under study. Although this time period has been traditionally considered acceptable for supporting long range system planning efforts, it limits the usefulness of a simulation model for making short-term operational adjustments, or for quickly evaluating the impact of sudden unexpected changes in the business environment on the computing infrastructure.
Lower the cost per model, which is the primary driver of the costs incurred in the process of answering concise performance or capacity questions. Developing a discrete event simulation that models a production IT environment is a resource intensive activity requiring experienced performance engineers to gather and evaluate data regarding the system targeted for study, measure and characterize the workloads imposed on the system, devise a model of the system in a computer-executable format, validate the effectiveness of the model, design and conduct a series of experiments using the model, and compile and interpret the results produced by the model. The nature of techniques required to create a discrete event simulation makes it difficult to change the resource intense nature of developing a new model.
It would be advantageous to institute a type of modeling factory that employs processes and techniques that would substantially reduce the overall time required to answer specific performance and capacity questions that are posed by business clients. This would include establishing the means to retain, recombine, and extensively reuse various simulation models that have been previously developed. Such models could be quickly applied with relatively few changes in updated experimental scenarios.
It would further be advantageous to provide a type of modeling factory using tools and techniques that reduce the cost of devising a simulation model to respond to specific performance and capacity questions, by reducing the amount and cost of resources consumed in developing and applying comprehensive models of commonly used business functions, systems, or services.